The Autonomic Nervous System (ANS) is represented by the sympathetic and parasympathetic nervous systems (SNS and PSNS). These function in opposition to each other. The sympathetic system typically functions to initiate immediate actions that require a quick physical response such as to quickly pull a hand away from a hot flame. In contrast, the parasympathetic system is associated with actions that do not require immediate reaction. Heart rate variability (HRV) is an established quantitative marker to obtain a quick superficial reflection of the state of the ANS. Heart rate variability is the beat-to-beat time variation in heart beat and is modulated primarily by the ANS via changes in the balance between parasympathetic and sympathetic influences. Heart rate is automatically adjusted in response to stress, respiration, metabolic changes, thermo-regulation, physical exertion, endocrine cycles, and the like. HRV is also useful for the diagnosis of various diseases and health conditions such as diabetic neuropathy, cardiovascular disease, myocardial infarction, fatigue, sleep problems, and others.
Systems have arisen in this art to estimate HRV. For example, HRV can be estimated by monitoring the electrical activity of the heart using, for example, a contact-based electro-cardiogram device (ECG) where HRV signals are generated by extracting the intervals between R-waves from the ECG. Due to the complex morphology of the ECG signals, locating the R-waves and its peaks to find the time intervals between peaks (RR interval) can result in an erroneous HRV result. Ectopic beats, arrhythmic events, missing data, environmental noise, and the like, may also contribute to inaccurate HRV measurements. When compared to contact-based ECG methods, non-contact methods provide the practitioner with the additional flexibility of being able to access different regions of the body where it may be hard to get signals using wires and probes. Moreover, signals obtained via non-contact methods can be improved by integrating over a larger region of interest when compared to contact-based ECG systems since ECG only provides signals that are localized to relatively small regions where the sensors were attached. Unobtrusive, non-contact methods to obtain HRV measures from a resting cardiac patient are desirable in this art.
Accordingly, what is needed in this art is a video-based system and method for estimating heart rate variability from time-series signals generated from video images captured of a subject of interest being monitored for cardiac function.